Data-mining previous surveys to improve walleye pollock target strength estimates

2021 ◽  
Vol 150 (4) ◽  
pp. A255-A255
Author(s):  
Nathan Lauffenburger ◽  
Alex De Robertis ◽  
Kresimir Williams
Keyword(s):  
Data Mining ◽  
Walleye Pollock ◽  
Target Strength

scholarly journals The influence of ontogeny, physiology, and behaviour on the target strength of walleye pollock (Theragra chalcogramma)

2003 ◽  
Vol 60 (5) ◽  
pp. 1063-1074 ◽  
Author(s):  
John K Horne
Keyword(s):  
Adult Life ◽  
Dorsal Surface ◽  
Walleye Pollock ◽  
Target Strength ◽  
Theragra Chalcogramma ◽  
Physical Factors ◽  
Biological Factors ◽  
Factors Associated ◽  
Surface Areas ◽  
Walleye Pollock Theragra Chalcogramma

Abstract Variability in echo intensities from aquatic organisms is caused by physical factors associated with the transmission of sound through water, and by biological factors associated with the ontogeny, physiology, and behaviour of targets. Acoustic-based density estimates depend on accurately characterizing reflected or backscattered sound from any species of interest. Digitized lateral and dorsal radiographs of walleye pollock (Theragra chalcogramma) were used to characterize intra-specific variability among young-of-the-year, juvenile, and adult life-history stages. Kirchhoff-ray mode (KRM) models were used to quantify variability in backscatter intensities at 38 and 120 kHz. At these geometric scattering frequencies, swimbladder surface areas influence echo intensities. Dorsal swimbladder surface areas were proportionate to fish lengths and decreased after fish were fed. Corresponding changes in backscatter were not proportionate to the reduction in dorsal surface area. The ratio of dorsal to lateral swimbladder surface areas was consistent among gravid and non-gravid fish. Walleye pollock tilt angles were centred at 90° and did not differ among densities or between light and dark cycles. Target strength–length regressions and KRM-predicted backscatter models closely matched in situ target-strength measurements for walleye pollock in the Bering Sea. Backscatter variability can be minimized through judicious deployment of equipment and equipment-parameter settings, but the relative influence of biological factors on backscatter amplitude has not been determined.

scholarly journals Evaluating total uncertainty for biomass- and abundance-at-age estimates from eastern Bering Sea walleye pollock acoustic-trawl surveys

2016 ◽  
Vol 73 (9) ◽  
pp. 2208-2226 ◽  
Author(s):  
Mathieu Woillez ◽  
Paul D. Walline ◽  
James N. Ianelli ◽  
Martin W. Dorn ◽  
Christopher D. Wilson ◽  
...  
Keyword(s):  
Bering Sea ◽  
Walleye Pollock ◽  
Target Strength ◽  
Acoustic Backscatter ◽  
Trawl Survey ◽  
Data Types ◽  
Total Uncertainty ◽  
Functional Relationships ◽  
Eastern Bering Sea ◽  
Trawl Surveys

Abstract A comprehensive evaluation of the uncertainty of acoustic-trawl survey estimates is needed to appropriately include them in stock assessments. However, this evaluation is not straightforward because various data types (acoustic backscatter, length, weight, and age composition) are combined to produce estimates of abundance- and biomass-at-age. Uncertainties associated with each data type and those from functional relationships among variables need to be evaluated and combined. Uncertainty due to spatial sampling is evaluated using geostatistical conditional (co-) simulations. Multiple realizations of acoustic backscatter were produced using transformed Gaussian simulations with a Gibbs sampler to handle zeros. Multiple realizations of length frequency distributions were produced using transformed multivariate Gaussian co-simulations derived from quantiles of the empirical length distributions. Uncertainty due to errors in functional relationships was evaluated using bootstrap for the target strength-at-length and the weight-at-length relationships and for age–length keys. The contribution of each of these major sources of uncertainty was assessed for acoustic-trawl surveys of walleye pollock in the eastern Bering Sea in 2006–2010. This simulation framework allows a general computation for estimating abundance- and biomass-at-age variance–covariance matrices. Such estimates suggest that the covariance structure assumed in fitting stock assessment models differs substantially from what careful analysis of survey data actually indicate.

Sign in / Sign up

Export Citation Format

Share Document